Classification of sentiment reviews using n-gram machine learning approach

Abstract— With the ever increasing social networking and online marketing sites, the reviews and blogs obtained from those, act as an important source for further analysis and improved decision making. These reviews are mostly unstructured by nature and thus, need processing like classification or clustering to provide a meaningful information for future uses. These reviews and blogs may be classified into different polarity groups such as positive, negative, and neutral in order to extract information from the input dataset. Supervised machine learning methods help to classify these reviews. In this paper, four different machine learning algorithms such as Naive Bayes (NB), Maximum Entropy (ME), Stochastic Gradient Descent (SGD), and Support Vector Machine (SVM) have been considered for classification of human sentiments. The accuracy of different methods are critically examined in order to access their performance on the basis of parameters such as precision, recall, f-measure, and accuracy. Sentiment analysis, also known as opinion mining, analyses people’s opinion as well as emotions towards entities such as products, organizations, and their associated attributes. In the present day scenario, social media play a pertinent role in providing information about any product from different reviews, blogs, and comments. In order to derive meaningful information from people’s sentiments, different machine learning techniques are applied by scholars and practitioners < final year projects >